@inproceedings{4cb298741a5b425b853f0e270376c4f3,
title = "Study of Manifestation of Civil Unrest on Twitter",
abstract = "Twitter is commonly used for civil unrest detection and forecasting tasks, but there is a lack of work in evaluating how civil unrest manifests on Twitter across countries and events. We present two in-depth case studies for two specific large-scale events, one in a country with high (English) Twitter usage (Johannesburg riots in South Africa) and one in a country with low Twitter usage (Burayu massacre protests in Ethiopia). We show that while there is event signal during the events, there is little signal leading up to the events. In addition to the case studies, we train n-gram-based models on a larger set of Twitter civil unrest data across time, events, and countries and use machine learning explainability tools (SHAP) to identify important features. The models were able to find words indicative of civil unrest that generalized across countries. The 42 countries span Africa, Middle East, and Southeast Asia and the events range occur between 2014 and 2019.",
author = "Abhinav Chinta and Jingyu Zhang and Alexandra DeLucia and Mark Dredze and Buczak, {Anna L.}",
note = "Publisher Copyright: {\textcopyright} 2021 Association for Computational Linguistics.; 7th Workshop on Noisy User-Generated Text, W-NUT 2021 ; Conference date: 11-11-2021",
year = "2021",
language = "English (US)",
series = "W-NUT 2021 - 7th Workshop on Noisy User-Generated Text, Proceedings of the Conference",
publisher = "Association for Computational Linguistics (ACL)",
pages = "396--409",
editor = "Wei Xu and Alan Ritter and Tim Baldwin and Afshin Rahimi",
booktitle = "W-NUT 2021 - 7th Workshop on Noisy User-Generated Text, Proceedings of the Conference",
address = "United States",
}